Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
1. Calibrating the camera¶
Read all the calibration images
In [1]:
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
%matplotlib inline
# Read all the clibration images
image_fnames = glob.glob('camera_cal/calibration*.jpg')
images = []
Find the chessboard corners and draw them
In [2]:
# Arrays to store object points and image points from all the images
objpoints = [] # 3D points in real world space
imgpoints = [] # 2D points in image space
# initialize the object points as (0,0,0), (1,0,0), ..., (8,5,0)
objp = np.zeros((6*9, 3), np.float32)
objp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1,2) # x, y coordinates
for fname in image_fnames:
# read in each image
img = mpimg.imread(fname)
images.append(img)
# convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9, 6), None)
# if corners are found, append object points, image points
if ret == True:
imgpoints.append(corners)
objpoints.append(objp)
#draw and display the corners
img = cv2.drawChessboardCorners(img, (9, 6), corners, ret)
plt.figure()
plt.imshow(img)
Calibrate the camera using cv2.calibrateCamera
In [3]:
# perform the calibration
shape = images[0].shape[:2]
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, shape, None, None)
Verify that the calibration works
In [4]:
# pick a calibration image and undistort it to check
undistorted = cv2.undistort(images[11], mtx, dist, None, mtx)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(images[11])
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(undistorted)
ax2.set_title('Undistorted Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
2. Undistort the test images¶
In [5]:
def undistort(image):
undistorted = cv2.undistort(image, mtx, dist, None, mtx)
return undistorted
In [6]:
import glob
files = glob.glob('test_images/*.jpg')
original_images = []
undistorted_images = []
for file in files:
image = mpimg.imread(file)
original_images.append(image)
image_undistorted = undistort(image)
undistorted_images.append(image_undistorted)
Compare the original image and undistored image
In [7]:
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(original_images[1])
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(undistorted_images[1])
ax2.set_title('Undistorted', fontsize=40)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
3. Thresholding¶
Define the pipeline
In [266]:
def color_thresh(image, thresh=(0,255)):
R = image[:,:,0]
G = image[:,:,1]
B = image[:,:,2]
binary = np.zeros_like(R)
binary[(R >= thresh[0]) & (G >= thresh[0]) & (R <= thresh[1]) & (G <= thresh[1])] = 1
return binary
def hsv_thresh(image, thresh=(160, 255)):
# Convert to HSV color space
hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV).astype(np.float)
s_channel = hsv[:,:,1]
v_channel = hsv[:,:,2]
binary = np.zeros_like(v_channel)
binary[(v_channel >= thresh[0]) & (v_channel <= thresh[1])] = 1
return binary
def hls_thresh(image, thresh=(50, 200)):
# Convert to HLS color space
hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS).astype(np.float)
l_channel = hls[:,:,1]
s_channel = hls[:,:,2]
binary = np.zeros_like(s_channel)
binary[(s_channel >= thresh[0]) & (s_channel <= thresh[1])] = 1
# binary[(l_channel >= thresh[0]) & (l_channel <= thresh[1])] = 1
return binary
def abs_sobel_thresh(image, orient='x', sobel_kernel=3, thresh=(0, 255)):
# 1) Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY).astype(np.float)
# 2) Take the derivative in x or y given orient = 'x' or 'y'
if orient == 'x':
sobel = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
if orient == 'y':
sobel = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Take the absolute value of the derivative or gradient
abs_sobel = np.absolute(sobel)
# 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# 5) Create a mask of 1's where the scaled gradient magnitude
# is > thresh_min and < thresh_max
binary_output = np.zeros_like(scaled_sobel)
binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
def mag_thresh(image, sobel_kernel=3, mag_thresh=(0, 255)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY).astype(np.float)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
# 3) Calculate the magnitude
sobelmag = np.sqrt(np.power(sobelx,2) + np.power(sobely,2))
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
scaled_sobel = np.uint8(255*sobelmag/np.max(sobelmag))
# 5) Create a binary mask where mag thresholds are met
binary_output = np.zeros_like(scaled_sobel)
binary_output[(scaled_sobel >= mag_thresh[0]) & (scaled_sobel <= mag_thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
def dir_threshold(image, sobel_kernel=3, thresh=(0, np.pi/2)):
# Apply the following steps to img
# 1) Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY).astype(np.float)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Take the absolute value of the x and y gradients
sobelx_abs = np.absolute(sobelx)
sobely_abs = np.absolute(sobely)
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
direction = np.arctan2(sobely_abs, sobelx_abs)
# 5) Create a binary mask where direction thresholds are met
binary_output = np.zeros_like(direction)
binary_output[(direction >= thresh[0]) & (direction <= thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
def sobel_thresh(image):
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
ksize = 3
gradx = abs_sobel_thresh(image, orient='x', sobel_kernel=ksize, thresh=(30, 100))
grady = abs_sobel_thresh(image, orient='y', sobel_kernel=ksize, thresh=(30, 100))
mag_binary = mag_thresh(image, sobel_kernel=ksize, mag_thresh=(30, 100))
dir_binary = dir_threshold(image, sobel_kernel=ksize, thresh=(0.7, 1.3))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
return combined
# Edit this function to create your own pipeline.
def pipeline(image):
sobel_binary = sobel_thresh(image)
color_binary = color_thresh(image, thresh=(190, 255))
s_channel_binary = hls_thresh(image, thresh=(90, 255))
# Combine the two binary thresholds
combined_binary = np.zeros_like(s_channel_binary)
combined_binary[(sobel_binary == 1) | (color_binary == 1) | (s_channel_binary == 1)] = 1
return combined_binary
In [284]:
image = undistorted_images[-1]
sobel_binary = sobel_thresh(image)
color_binary = color_thresh(image, thresh=(190, 255))
s_channel_binary = hls_thresh(image, thresh=(90, 255))
combined = pipeline(image)
plt.figure(figsize=(24,9))
plt.imshow(image)
plt.show()
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(s_channel_binary, cmap='gray')
ax1.set_title('S Channel Threshold', fontsize=40)
ax2.imshow(sobel_binary, cmap='gray')
ax2.set_title('Sobel Threshold', fontsize=40)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(color_binary, cmap='gray')
ax1.set_title('Color Threshold', fontsize=40)
ax2.imshow(combined, cmap='gray')
ax2.set_title('Combined', fontsize=40)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
# fig = plt.figure(figsize=(24, 9))
# fig.add_subplot(1,2,1)
# plt.imshow(image)
# fig.add_subplot(1,2,2)
# plt.imshow(s_channel_binary, cmap='gray')
# plt.show()
# fig = plt.figure(figsize=(24, 9))
# fig.add_subplot(1,2,1)
# plt.imshow(color_binary, cmap='gray')
# fig.add_subplot(1,2,2)
# plt.imshow(sobel_binary, cmap='gray')
# plt.show()
Visualize the pipeline
In [268]:
binary_images = []
for image_undistorted in undistorted_images:
image_binary = pipeline(image_undistorted)
binary_images.append(image_binary)
fig = plt.figure(figsize=(24, 9))
fig.add_subplot(1,2,1)
plt.imshow(image_undistorted)
fig.add_subplot(1,2,2)
plt.imshow(image_binary, cmap='gray')
plt.show()
4. Perspective Transform¶
In [269]:
def perspectiveTransform(img):
src = np.float32([[200, 720], [550, 480], [730, 480], [1080, 720]])
dst = np.float32([[200, 720], [200,0], [1080,0], [1080, 720]])
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
img_size = (img.shape[1], img.shape[0])
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
return warped, M, Minv
def perspectiveRestore(img, Minv):
img_size = (img.shape[1], img.shape[0])
restored = cv2.warpPerspective(img, Minv, img_size, flags=cv2.INTER_LINEAR)
return restored
Visualize perspective transformation
In [270]:
top_downs = []
binary_top_downs = []
for idx, undistorted_image in enumerate(undistorted_images):
binary_image = binary_images[idx]
top_down, M, Minv = perspectiveTransform(undistorted_image)
top_down_binary, M, Minv = perspectiveTransform(binary_image)
top_downs.append(top_down)
binary_top_downs.append(top_down_binary)
fig = plt.figure(figsize=(16, 9))
fig.add_subplot(1,4,1)
plt.imshow(undistorted_image)
fig.add_subplot(1,4,2)
plt.imshow(binary_image, cmap='gray')
fig.add_subplot(1,4,3)
plt.imshow(top_down)
fig.add_subplot(1,4,4)
plt.imshow(top_down_binary, cmap='gray')
plt.show()
4. Detect lane lines¶
In [187]:
# start searching for the lane lines from scratch
def detect_lane_lines_cold(binary_warped):
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
# out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
# cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
# cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = [0,0,0]
right_fit = [0,0,0]
if (len(lefty) > 0 and len(leftx) > 0):
left_fit = np.polyfit(lefty, leftx, 2)
if (len(rightx) > 0 and len(righty) > 0):
right_fit = np.polyfit(righty, rightx, 2)
return left_fit, right_fit, left_lane_inds, right_lane_inds
In [188]:
# search for the lane lines given previous positions of the lane lines
def detect_lane_lines_hot(binary_warped, prev_left_fit, prev_right_fit):
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (prev_left_fit[0]*(nonzeroy**2) + prev_left_fit[1]*nonzeroy + prev_left_fit[2] - margin)) & (nonzerox < (prev_left_fit[0]*(nonzeroy**2) + prev_left_fit[1]*nonzeroy + prev_left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (prev_right_fit[0]*(nonzeroy**2) + prev_right_fit[1]*nonzeroy + prev_right_fit[2] - margin)) & (nonzerox < (prev_right_fit[0]*(nonzeroy**2) + prev_right_fit[1]*nonzeroy + prev_right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
return left_fit, right_fit, left_lane_inds, right_lane_inds
Visualization
In [271]:
def draw_lane_fit_lines(top_down, binary_warped):
fig = plt.figure(figsize=(16, 9))
fig.add_subplot(1,2,1)
plt.imshow(top_down)
left_fit, right_fit, left_lane_inds, right_lane_inds = detect_lane_lines_cold(binary_warped)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Create an output image to draw on and visualize the result
out_img = np.zeros([binary_warped.shape[0], binary_warped.shape[1], 3], dtype=np.uint8)
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
fig.add_subplot(1,2,2)
plt.imshow(out_img)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
In [272]:
for idx, binary_top_down in enumerate(binary_top_downs):
top_down = top_downs[idx]
top_down_binary_fit = draw_lane_fit_lines(top_down, binary_top_down)
5. Measure Curvature¶
In [279]:
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
def calc_curvature(binary_warped, x, y):
# Fit new polynomials to x,y in world space
fit_cr = np.polyfit(y*ym_per_pix, x*xm_per_pix, 2)
y_eval = binary_warped.shape[0]
curverad = ((1 + (2*fit_cr[0]*y_eval + fit_cr[1])**2)**1.5) / np.absolute(2*fit_cr[0])
curverad = np.round(curverad, 2)
return curverad
Define a Line class to store the history data
In [280]:
# Define a class to receive the characteristics of each line detection
class Line():
def __init__(self):
self.image = None
self.M = None
self.Minv = None
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted_left = []
self.recent_xfitted_right = []
#average x values of the fitted line over the last n iterations
self.bestx_left = None
self.besty_right = None
#polynomial coefficients averaged over the last n iterations
self.best_fit_left = None
self.best_fit_right = None
#polynomial coefficients for the most recent fit
self.current_fit_left = [np.array([False])]
self.current_fit_right = [np.array([False])]
#radius of curvature of the line in some units
self.radius_of_curvature_left = None
self.radius_of_curvature_right = None
self.radius_of_curvature_mid = None
#distance in meters of vehicle center from the line
self.line_base_pos = None
#difference in fit coefficients between last and new fits
self.diffs = np.array([0,0,0], dtype='float')
#x values for detected line pixels
self.allx_left = None
self.allx_right = None
self.allx_mid = None
#y values for detected line pixels
self.ally_left = None
self.ally_right = None
self.ally_mid = None
# vehicle's offset from the center of image
self.offset = None
def sanity_check(self, left_fit, right_fit):
# Generate x and y values for plotting
ploty = np.linspace(0, self.image.shape[0]-1, self.image.shape[0] )
leftx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
rightx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
midx = (leftx + rightx)/2
mid_fit = np.polyfit(ploty, midx, 2)
#1 check the left right curvature have the same sign, i.e. curving toward the same direction
left_curv = calc_curvature(self.image, leftx, ploty)
right_curv = calc_curvature(self.image, rightx, ploty)
mid_curv = calc_curvature(self.image, midx, ploty)
if ((left_curv > 0 and right_curv < 0) or (left_curv < 0 and right_curv > 0)):
return False
# check that they are not an order of magnitude different
ratio = np.absolute(left_curv/right_curv)
if (ratio > 10 or ratio < 0.1 ):
return False
#2 check that the distance between left and right lane is roughly the correct distance ~900 px
distance = 900
variance = 100
if (np.absolute((rightx[-1] - leftx[-1]) - distance) > variance):
return False
#3 check that lines are roughly parallel, measured by standard deviation
dist = rightx - leftx
std = np.std(dist)
if (std > 100):
return False
# Sanity check passed, update all the values
self.ally_left = self.ally_right = self.ally_mid = ploty
self.allx_left = leftx
self.allx_right = rightx
self.allx_mid = midx
self.radius_of_curvature_left = left_curv
self.radius_of_curvature_right = right_curv
self.radius_of_curvature_mid = mid_curv
lane_center = (leftx[-1] + rightx[-1]) / 2
image_center = self.image.shape[1] / 2
self.offset = np.absolute(lane_center - image_center) * xm_per_pix
self.offset = np.round(self.offset, 2)
return True
def find_lane_lines(self, binary_warped, M, Minv):
self.image = binary_warped
self.M = M
self.Minv = Minv
if (self.detected):
left_fit_prev = self.current_fit_left[-1]
right_fit_prev = self.current_fit_right[-1]
left_fit, right_fit, left_lane_inds, right_lane_inds = detect_lane_lines_hot(binary_warped, left_fit_prev, right_fit_prev)
else:
left_fit, right_fit, left_lane_inds, right_lane_inds = detect_lane_lines_cold(binary_warped)
if (self.sanity_check(left_fit, right_fit)):
# pass
self.detected = True
else:
# not pass
self.detected = False
# use the last one as current fit
left_fit = self.current_fit_left[-1]
right_fit = self.current_fit_right[-1]
self.current_fit_left.append(left_fit)
self.current_fit_right.append(right_fit)
# only keep the last 10 set of coefficiencies
if (len(self.current_fit_left) > 10):
self.current_fit_left = self.current_fit_left[-10:]
if (len(self.current_fit_right) > 10):
self.current_fit_right = self.current_fit_right[-10:]
# get the average of the previous 10 coefficiencies
self.best_fit_left = np.mean(self.current_fit_left, axis=0)
self.best_fit_right = np.mean(self.current_fit_right, axis=0)
return self.best_fit_left, self.best_fit_right
def get_curvature():
return self.radius_of_curvature_left, self.radius_of_curvature_right, self.radius_of_curvature_mid
def draw_lane_region(self):
# Create an blank image
warp_zero = np.zeros_like(self.image).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Generate a polygon to mark the region between the left lane and right lane
left_line_pts = np.array([np.transpose(np.vstack([self.allx_left, self.ally_left]))])
right_line_pts = np.array([np.flipud(np.transpose(np.vstack([self.allx_right, self.ally_right])))])
lane_region_pts = np.hstack((left_line_pts, right_line_pts))
# Draw the lane region onto the warped blank image
cv2.fillPoly(color_warp, np.int_([lane_region_pts]), (0,255, 0))
# Perspective transform back to 3D
perspective_restored = perspectiveRestore(color_warp, self.Minv)
return perspective_restored
def annotate_image(self, image):
curv_str = 'curvature: ' + str(self.radius_of_curvature_mid) + 'm'
offset_str = 'offset: ' + str(self.offset) + 'm'
font = cv2.FONT_HERSHEY_COMPLEX
scale = 1
white = (255, 255, 255)
thickness = 2
cv2.putText(image, curv_str, (30, 120), font, scale, white, thickness)
cv2.putText(image, offset_str, (30, 150), font, scale, white, thickness)
return image
6. Complete pipeline¶
In [281]:
line = Line()
def process_image(image):
undistorted = undistort(image)
binary = pipeline(undistorted)
top_down, M, Minv = perspectiveTransform(binary)
left_fit, right_fit = line.find_lane_lines(top_down, M, Minv)
lane_image = line.draw_lane_region()
result = cv2.addWeighted(undistorted, 1, lane_image, 0.3, 0)
line.annotate_image(result)
return result
7. Test on video¶
In [282]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
output_file = 'output_videos/project_video.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
clip = VideoFileClip("project_video.mp4").subclip(38,48)
# clip = VideoFileClip("project_video.mp4")
write_clip = clip.fl_image(process_image) #NOTE: this function expects color images!!
%time write_clip.write_videofile(output_file, audio=False)
In [283]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(output_file))
Out[283]:
In [143]:
output_file2 = 'output_videos/challenge_video.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
clip2 = VideoFileClip("challenge_video.mp4")
write_clip2 = clip2.fl_image(process_image) #NOTE: this function expects color images!!
%time write_clip.write_videofile(output_file2, audio=False)
In [169]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(output_file2))
Out[169]:
In [ ]: